July 15, 2025 • 52 mins
Self-driving cars have been heralded by many as the future of transportation—promising safer roads, reduced traffic, and greater mobility for all. Yet despite rapid advancements in technology and a surge in assistive driving features, acceptance of full vehicle automation remains elusive. As the public grapples with questions of trust, safety, and responsibility, the road to widespread adoption of automated vehicles is anything but straightforward. The stakes are high: how we navigate this transition could redefine the way we move for generations to come. So, how far are we from acceptance, adoption, and implementation?
In this episode, we’re joined by the co-directors of the Advanced Vehicle Technology (AVT) Consortium, hosted within the MIT AgeLab at the MIT Center or Transportation & Logistics: Dr. Bryan Reimer, Dr. Pnina Gershon, and Dr. Bruce Mehler. They explore key insights from their recent research, the role of data in shaping safer and smarter mobility solutions, and how the consortium is addressing critical questions around driver behavior, automation readiness, and industry collaboration as they celebrate their 10th anniversary year.
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Episode Transcript
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(00:00):
(upbeat music)
- Welcome to another episodeof "Supply Chain Frontiers,"
the MIT CTL podcast
where we explore the trends,technologies and innovations
shaping the future ofsupply chain management.
I'm your host for thisepisode, Mackenzie Berry.
Today, we're diving into the world
of Advanced Vehicle Technology
and its implications forsupply chains and mobility.
(00:23):
Joining me are leading experts
from the MIT Center forTransportation & Logistics,
Dr. Bryan Reimer,
Founder and Co-director
of the Advanced VehicleTechnology Consortium,
Dr. Pnina Gershon,
Co-director of the AVT Consortium,
and Bruce Mehler,
Co-director of the AVT Consortium,
which is commemorating its10th anniversary this year.
(00:44):
Here's what I know.
AVT was founded with theaim of developing new data
that contributes toautomotive manufacturers',
suppliers' and insurers'real-world understanding
of how drivers use and respond
to increasingly sophisticatedvehicle technologies
such as assistive and automated driving
while accelerating theapplied insight needed
(01:04):
to advance design and development.
The Consortium has developeda deep understanding
of driver behavior andconsumer preferences
with technologies like Tesla's Autopilot,
GM's Super Cruise and Ford's BlueCruise,
as well as many other driver assistance
and support features.
Insights from AVT aimto help organizations
better design and market products
(01:24):
more closely aligning withreal-world consumer use
while advancing safe, convenientand sustainable mobility.
In this episode, Bryan, Pnina and Bruce
will explore key insightsfrom their recent research,
the role of data
in shaping safer andsmarter mobility solutions,
and how the Consortium isaddressing critical questions
around driver behavior,automation readiness,
(01:47):
and industry collaboration
as they celebrate theiranniversary milestone.
So welcome to the episode.
I'm so excited to have you all.
I introduced your titles,
but I would love if we could go around
and you all introduce yourself
with the work that you'lldo in the AVT Consortium.
- Once again, my name is Bruce Mehler,
I'm a Research Scientist
at the Center forTransportation & Logistics.
(02:08):
My own background is inpsychology and physiology,
and I've worked actually inthe medical device industry
for about 22 years before coming to MIT.
So I do have some appreciation
of the practical side of industry
in terms of actually having todevelop and produce product.
And I think that's areally important aspect
(02:29):
of working in a group like this at MIT,
where we're interested in bothhigh-quality academic work
that provides theoretical insights
while at the same time investing energy
and actually translating those insights
into operational informationthat industry can actually use.
(02:50):
And so I came to MIT aboutclose to 20 years ago now
and have been working inthis area since that time.
- Mackenzie, thanks for having us.
Bryan Reamer,
I've been a Research Scientist
within the Center forTransportation & Logistics
for about 22 years now.
I'm known as a pragmatic possibilist,
studying the intersection ofbehavior, technology and policy
(03:12):
at really the intersection of topics
around automation,assistive driving and AI.
Really focused on understanding
how we can encourage businessinnovation from research,
moving concept from the lab
to product innovations that touch us.
How do we create the ecosystems
that allow us to engagein the technologies
(03:32):
that can excite and delight the customer
and impact how we live and move
and leverage advanced technologies
as part of our daily lives?
So a lot of my background
comes from the study of driver behavior
and the evolution oftechnology in the auto sector
over the last two and a half decades.
- Hi, so my name is Pnina Gershon,
and I'm a ResearchScientist at the MIT AgeLab,
(03:53):
where I study how humansand technology interact
and how these interactions
impact mobility and driving safety.
I Co-direct the AdvancedVehicle Technology Consortium,
leading the research onnaturalistic driving.
So I bring many years ofexperience in data science,
AI, driving simulation,computational modeling,
(04:15):
and human physiology.
Before joining the AgeLab at MIT,
I was working on drivingsafety for high-risk population
at the National Institutes of Health,
and before that I was focused
on tactile and haptic interactions.
- Wonderful.
Bryan, I introduced the Consortium a bit,
(04:35):
but better to hear in your own words.
Can you give us a briefoverview of the Consortium,
how it started and whatits primary goals are?
- Sure, Mackenzie.
What we discovered withour industry partners
was that there was a lack of information
that existed in federaland research databases
around how consumers weretouching data in real cars
(04:56):
once they took ownership of the dealer.
So cars designed, developed
with incredible amounts of research,
investment, product development,
product planning, packaging,
and ultimately moving a car to the dealer.
But once the keys arehanded to the consumer,
there's very little understood,
and that white space waswhat AVT was built upon.
(05:18):
It was built as an academicindustry collaboration
informed by industry for industry.
How do we derive the insight
in what people areleveraging in their own lives
to help enhance thedevelopment of product,
the impact of the productsthat are being developed,
and the policies thatwill long-term govern
this industry forward?
(05:39):
It was captivated asAdvanced Vehicle Technology
because at that time we knew
automated, assisted were narrowly focused.
We wanna be thinking aboutthe wide swath of technology
that over the course of several decades
may be shaping how we live and move,
so very much the mobilitystory much more broadly.
- And if you could take usbehind the scenes a little bit,
(06:01):
what methodologies and research techniques
does the Consortium useto study driver behavior?
- So at the AVT Consortium,
we focus heavily onnaturalistic driving studies.
That means we are collectingdata from real people
driving real roads intheir own environments
and doing their regular routine.
(06:22):
So we take two main approaches for this.
The first we look atlong-term study of volunteers,
people that allow us toinstrument their own vehicles
with data acquisition systems.
Many of these participants havebeen with us for years now,
and that gives us a rare opportunity
(06:44):
to look at how behavior evolves over time,
so months and years.
We can study how peopleadapt to new features,
but also how they respondto software updates
and how trust in automation
evolves over these long periods of time.
Second, we run field operational tests,
(07:04):
which basically we haveour own vehicle fleet,
the MIT AgeLab vehicles,
in this case we loan our vehicles
like we have Volvo S90s,
Tesla Model 3s, Cadillac CT6, and so on.
So all of them are equipped
with Advanced Vehicle Technologies,
(07:25):
and participants use these cars
just like as if they woulduse their own vehicles
for their daily routine.
Now that's a great wayto learn how novices
or naive users get used to automation
or interact with automation.
So we look closely at early learning
(07:47):
and first impressionsand how people explore
or avoid using advanced technologies.
- Yeah, interesting.
Not only how they use it,
but what they avoid using as well,
which can tell you a lot.
Now, I imagine,
and I'm sure our listeners as well,
that you're dealingwith a lot of data here.
So curious, what are someof the biggest challenges
in working with large-scalenaturalistic driving data
(08:08):
and how do you ensure dataquality and reliability?
- Well, I think that, asyou said in the question,
the biggest challenge aroundworking with naturalistic data
is that it can be very, very large.
Particularly in our case,
depending on the vehiclethat we're working with,
we're using anywhere fromthree to four cameras
in the vehicle all the time.
(08:30):
And so if you can imagine
that literally you havehigh resolution cameras,
three or four in eachvehicle collecting data
every time the person goes and drives
for a month or two months or three months
or longer in time,
that data really builds up quickly.
And actually one of thethings that allows us
(08:52):
to pragmatically do thework that we're doing
is because of our investment through MIT
and through the state
in the Massachusetts HighPerformance Computing Center,
we actually have accessto a whole farm of servers
and large data storage.
In fact, if we didn't have access to that,
(09:14):
we could not work with the data
that's as large as we're using.
In fact, when we firststarted doing this work
and before we moved
to the High Performance Computing Center,
we were basically the second largest user
of storage space here on the MIT Campus.
I think astrophysics was the largest,
and all the rest of us were small by size,
(09:36):
but we were number two for a long time.
So once again, the biggest challenge
really is that size of the data,
processing the data, storing the data,
curating the data.
And I think in terms of data quality
and reliability insurance,
my colleagues Bryan or Pnina
may have something to add on that.
- Yeah, I mean that's a great question.
(09:57):
Honestly, this is somethingthat we think about every day
when you're collectingthis huge amount of data
and mixed data,
including video, audio,vehicle signals, GPS,
everything that Bruce just mentioned.
You're not just managingthe technical complexity,
but you also have the responsibility
(10:18):
to protect the people that volunteered
to share their driving with you.
So a lot of our effortis really technical,
but also basically making sure
that we have qualityassurance processes in place
that combine statistical tools
and human efforts toensure that the data we use
(10:38):
for research is intact and safe.
- I think one thing to add isthat the size of the datasets
that we collect are in thehundreds of terabytes range,
which by industry standards is modest,
by academic standards is extremely large.
It requires softwareengineering, data security,
computational investments
that are not traditionallyfound on college campuses.
(10:59):
And as Bruce mentioned earlier,
it's really MIT's forwardthinking investments
with the state of Massachusetts,
a few of the old colleges, universities
that have really enabledAVT to be what it is.
- And speaking of that,
you all just celebratedyour 10th anniversary,
which is a major milestone,celebrating it this year,
and held an event here on campus
(11:20):
which brought together major stakeholders
from across the automotive industry.
Curious, and if you could share
your biggest takeaway from the event.
- I think it was a sentinel event.
I think the goal that we had
was to really move the conversation
out of the analytical elements
that AVT has been focusing on or a decade
and move it back up a levelor two to the strategic level.
(11:40):
And the panels and keynotes
are all available off of our website,
so in long form.
But it really sharedwith me and highlighted
that the white space
in automotive safety research is shifting.
There's been a lot of focuson the use of technology
to try to encourage andimprove driver behavior,
but there is a lot of new elements
(12:02):
that technology is uncovering here.
And the repair vehicles gettingto be too expensive per se,
we're totaling way too many vehicles.
The context of sensor calibration,
when you do have a car
that needs this windshield replaced,
the amount of hours of labor
that needs to go intocalibrating those sensors
so they actually work correctly again
and the unknowns of a usedvehicle that you may purchase
(12:24):
and whether the sensors wereactually calibrated correctly
or the consumer found a runaround
to a cheaper repair facility.
Conversation with John Bozzella,CEO of Auto Innovators,
and Mark Rosekind, formerAdministrator of NITSA,
really focusing on the complexities
of vehicle safety policy looking forward.
(12:45):
But I think the most importanttakeaway and discussion point
is that the United Statesand the Western world
no longer leads in the area
of Advanced Vehicle Technologies,
China does.
The explosion of the Chinese auto industry
in the last five or more years
and the movement and acceleration
of assisted and automated driving features
(13:06):
into the fleet is astonishing.
The Chinese government back in late April,
beginning to crack down
and set some standardsaround assisted driving
that really lead the world,
thinking about the innovation in China
being curtailed by regulation
to a degree that in the United States
and most of the European nationshasn't been touched on yet.
(13:27):
So when we look forward,
we need to be thinking fromour perspective here at MIT
and in the United States ofhow do we accelerate forward?
And I believe very stronglythat collaborations
and partnerships are wherewe're gonna have to get there.
We're gonna have to bringindustry and academia,
industry and industry back together
to recapture our leadershipin this area quickly.
(13:49):
- Well, and as I understand it, Bruce,
you attended the Shanghai Auto Show
as well as the Detroit one.
What did you notice in termsof difference between the two
that Bryan had just spoke to?
- It was totally eye-opening.
As you said, I went to theDetroit Auto Show in January,
and it was a nice experience,
lots of interesting new cars on display,
lots of interesting booths and so on.
(14:11):
And then about two weeks
before our event in May,
I was in Shanghai
and went to the Auto Show there
and was bowled over.
The Auto Show in terms of space
was at least three to four times larger
than the Detroit Auto Show.
(14:33):
The number of vehicleswere three to four times
those that were presentin the Detroit Auto Show.
And it was amazing
looking at the evolutionthat's taken place,
not only in the vehicles themselves,
but the relationship betweenthe Chinese automakers
and the European automakers.
So in fact, there were anumber of companies there
(14:56):
presenting who did have partnerships
with the European automakers.
And you could see whathas changed over time
from the Europeanautomakers being the leaders
and having some slightlyless expensive versions
produced in China.
It's really turned around
where the Chinese automakers
(15:18):
who have still partnerships
were actually the predominantpeople at the show,
and their vehicles were larger,
fancier, equipped with more features
than their European partners.
So clearly a very rapidly changing space,
very much as Bryan has indicated.
One of the things that did come out
(15:40):
from both being at the Shanghai Auto Show,
but I also spent a couple weeks
at Tongji University in Shanghai,
which is really the leading university
for automotive studies in China.
And one of the interesting things
that came out from that
is talking to colleagues there
about the work that they were doing.
(16:00):
One of the areas that did stand out
where we are still leading in a sense
is that naturalistic data collection
in real vehicles by customers on the road
is relatively unknown at this point.
And so in fact, the AVTefforts in that particular area
of studying humaninteraction on the real road
(16:22):
with real drivers is stillan area where we are leading,
and it's important tocontinue that emphasis.
- Yeah, absolutely.
And turning to public perspective,
I imagine you all think alot about this in your work
and maybe have to dispel some things
among people that you know
right in conversation withthe expertise that you have,
but what are some misconceptionsabout vehicle automation
(16:42):
and driver behavior thatyou encounter most often?
- So one of the biggest misconception
is that automation makes driving passive.
In reality, most systems still require
an active driver and active supervision,
and drivers often dointeract with the automation.
Another is a belief
(17:03):
that more automationnecessarily mean more safety.
So driver behavior, trust,
and how systems are actually used
play a huge role in the outcome.
And automation can support the driver,
but it doesn't replace them,
so at least not yet.
- To me that we believe this aspect
(17:26):
of automation in vehicles is new.
It's not.
Everything from automatic transmissions
to power steering are thefoundations that this is built on.
So we've been actuallyinfusing automation into cars
in a variety of differentways for decades,
more and more, yeah.
But the offshoot ofthat is the expectation
that if you're gonna add more automation,
(17:48):
folks are going payattention the same way.
They're not going to,why are we automating?
We're automating to free resources.
Now the question is as humans,
how are we gonna behave?
How are we gonna use thoseresources to do other things?
And some of the data thatwe're beginning to see
and understand is that humansare capable of interacting
with the assistant systems in ways
(18:09):
that we wouldn't have expected early on.
So quite frankly, it'sless about the automation,
it's more about the interfacesand the support systems
and how to connect us to that automation.
Technology in and of itself
is not going to solve adriver behavior problem.
We keep adding more andmore technology to cars
with the hope and prayer
that it reduces accidents, mitigates harm.
(18:30):
The reality is we have to fit,
we have to begin to synctechnology focused very targetedly
at the behaviors we wanna optimize.
We, as humans, are notall above average drivers,
although I'm sure many of usaround the table here today
and listening are thinking
"Everybody's gotta be above average,"
but 50% is average.
- Yeah, absolutely.
No, I think we can all be on one page
that we don't like the Boston traffic
(18:51):
and the many drivers that we encounter,
I think everyone can agree with that.
- I'll take the Boston drivers
over the New York drivers every day.
- See that's a hot debate topic,
which city has the worst drivers?
Which people often debatein the public sphere.
Well, and public perceptionis a critical factor,
perhaps the most critical factor
in assisted and automatedvehicle adoption.
(19:12):
What does y'all's research suggest
about the biggest barriersto public acceptance?
- One of the biggest barriers
that's that's fairly well discussed openly
is consumers understandingof what assisted
and automated drivingis in the United States.
A recent piece out inthe last couple weeks
suggests that that's one of the bigger
differentiating piecesbetween consumers in China
(19:32):
and consumers in the States
is that consumers in China
really know what all this technology is,
and are getting a much more
mainstream understanding of that,
so they're demanding it.
And the US consumers don'treally have clear understanding
of what's assistive, what's automated,
and the fact that fullself-driving doesn't really exist.
So when we think about that barrier,
(19:53):
we really think about trust,
trust in the technologiesthat's appropriately calibrated
to the system's support for us,
and ensuring that we're notoverly trusting technology
when we, as a human,would be a better driver,
but we're leveraging that technology
where the automation cansupport us most effectively.
- I can add to that also predictability.
(20:13):
So people want to know whatthe system is gonna do,
and they want to feel confident
that it will behave the waythey expect it to behave.
So this is one another key thing,
and another thing is we need to make
automation more personal.
So seeing in a video or reading about it
is not the same as experiencing.
(20:36):
So a short ride,
10 minutes in a Waymo car, for example,
can do more to shift someone's perception
than hours of media coverage.
It's about helping people build
the firsthand understanding.
That's where real acceptance starts.
(20:57):
- And can you share for listeners
who may not know what Waymo is?
- So Waymo is a Level 4 vehicle
basically in certain areasin the United States,
these vehicles are deployed,
so basically you canschedule a ride with them
and fully autonomous,
there is no driver behind the wheel.
(21:17):
- Yeah, very scary for some people still,
which is why we're here.
But Bruce, what about for you
in terms of what research suggests
about the biggest barriersof public acceptance?
- One of the challenges that we've seen
with the introduction of new technologies
is how the consumer develops expectations
about how a system will work,
how do you actually use it?
(21:38):
In fact, there's another aspectthere of, "How do I use it?
How do I turn it on?"
So there's many technologies in the car
that may be very well engineered
in terms of their underlying function,
but quite often there are challenges
in terms of the user interface.
Obviously, the user interfacesare developed by engineers
(22:00):
who spent hours and hours
thinking about anddeveloping these interfaces,
and they understandhow the interface works
in and out, right and left
so that everything of course
is, "Intuitive," quote, unquote,
because they've beenworking with it for years.
And it's that classic challenge
(22:22):
just as you find in computer systems,
quite often when thenew feature comes out,
it's intuitive to the designer
because they've worked on designing it,
but it may or may not beintuitive to the end user.
If they are actually walked through it,
they go, "Oh, okay,
now I understand howthis is meant to work."
(22:44):
But unless we're given appropriate support
to help walk us throughthis new technology,
it's quite often the case
that a number of people will never use it.
And we also know very well
that once a person gets,for example, a new car,
if they don't try out a technology
(23:05):
in the first three or four weeks,
there's a very, very high probability
they will never turn it on
for the length of timethat they own their car.
And so just as it's a challenge
with computer technologies and the like,
in the vehicle,
the whole issue designing user interfaces
and finding ways to help talk the user
(23:27):
through the feature so they can try it out
and then intelligently decide
whether or not they want to use it or not.
And that human factors interface
of helping introduce and explain features
to get people to try them
is really a key challengefor the industry.
- I think one thing toadd to my colleagues here
(23:48):
is that when you thinkabout barriers here,
we really have highlyautomated technologies
on the road today in a number of cities.
Ms. Pnina described
most of these systems are supported
extensively by teleoperation,
someone in the back office
whose helping these robots on the road.
The barrier to public acceptance to me
is how safe is safe enough?
(24:09):
We have no yardstick on how safe
assisted and automated technologies
need to be to be accepted.
And one of the things that I call
on the regulatory side for
is helping us form that social norm
because a technology thatcan help improve safety
by three or four times what it is today
is something we probablyshould be endorsing.
And the question is, isthat safe enough tomorrow?
(24:31):
And my answer is no, we needto be doing better tomorrow.
But picking some reasonable targets
and iterating from that
and enhancing public acceptance over time.
Otherwise, every incident there is,
is a major news media circus,
and that's really the key to avoiding,
to enhancing publicacceptance of this technology.
- Yeah, well, this is a great segue
because speaking of regulation,
(24:52):
how are government policy and regulation
influencing Advanced Vehicle Technology
development and adoption?
You spoke to it a little bit,
but if you could expand.
- So unfortunately,government policies today
are not as effectively asnon-government policies
enhancing regulatory adoptionof these technologies.
So organizations likethe Insurance Institute
Highway Safety Consumer Reports,
Euro NCAP,
(25:13):
more non-government organizations
that are either encouraging the adoption
of these technologies a little faster.
We saw late in the last administration
reforms in the US NCAP program,
which is really NITSA,
National Highway TransportationSafety Administration,
their effort to proactivelypromote these technologies,
but it's a little later than optimal.
So it's really about using the carrot
(25:36):
and the education sideof promoting technologies
that we don't truly understandthe safety benefit of.
And that's where the whitespace I talked earlier
that we discovered nearlya decade and a half ago
was that many of these systems,
well, we believe they're good,
we don't know until the datais developed years later.
So one of the pieces several years ago
(25:56):
during the Obama administration
was the collaborative agreementbetween the automakers,
NITSA and IHS around the the deployment
of automated emergency braking systems,
which was really a Hail Mary pass
at trying to accelerate thesetechnologies on the road.
So at the end of the day,
we see the NGOs takingmore of a leadership
than the government here.
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(26:18):
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(26:41):
Bryan, you mentioned this earlier,
the cost of repairs
as technology becomesmore advanced in vehicles.
One of the panels that interestedme most at the AVT event
was the collision repair panel,
which was really meantto represent consumers,
and they spoke to the high cost of repair
as well as the insurance for vehicles
with assisted and automated technology.
What seems to be the consumerperception in this regard
(27:01):
and how should industry respond?
- Consumer perception isthat cars are repaired
much like they used to be,
and unfortunately they're not.
These are highly complex technical systems
integrating together a little sheet metal,
a little aluminum,
lots of different forms of plastic,
sensing technologies, compute, wiring.
These are among the mostcomplex technologies
that we as humans engagewith on a daily basis.
(27:25):
So when something happens,
it's not going and justbanging out the metal
and reforming the structureof the car that way,
it's about rebuildingthe sensor architecture
that was there to protect you
in the case of an adverse event.
So when we think about repair today,
it's not auto body repair,what it used to be.
It's bringing in a bunch of engineers
to figure out, "How do we address this?
(27:46):
Can we find the parts?
Where might those parts be coming from?"
And then quite frankly,
calibrating the systems again,
and hours and hoursand investment to that.
Things that take minutes
when the car is flowingoff the assembly line
take hours to recreate later on.
So when consumers think aboutrisk and insurance costs,
there's no mystery that thecost of ownership of a vehicle
(28:09):
starts with your purchase
if you can afford topurchase or lease a car,
but the cost of insuring that
and the lifecycle costs of that vehicle
are driven by the complexities
of sensing and compute technology.
So I think one of thethings that consumers
really don't recognize enough
is that the auto industry isadvancing in material sciences
at an accelerated pace.
(28:30):
How do you best way to save gas
is to make the vehicle lighter,
so huge efforts in lightweighting.
And the same is really therein a lot of other logistics
avenues as well, whetherthat's long haul trucking
or that's local delivery
or robots chasing us around on streets
to deliver packages in some cities today.
- Yeah, how do you thinkindustry should respond
given the higher costsnow on the consumers?
- I think it starts witha strategic conversation,
(28:52):
being willing to talk across the industry
on how do we find newstrategic pathways forward?
Quite frankly, most of theorganizations that I talk to
know there is a cost problem.
The question is how do you beginto address complex problems
that require so manystakeholders involved?
Each organization only owns
a little piece of this complexity.
(29:13):
So it's moving from a oneindustry talking to another
or one partner talking to another
to how do you get key players in a room
to have a real big picture conversation,
setting strategy forward
where we're really thinking about repaving
or restructuring how we lookat the cost of ownership,
the cost of repair of vehicles,
maybe even the inspection
and licensure of repair's facilities.
(29:35):
The technologies requiredare far different.
And this brings all topicssuch as right to repair,
which has been hot in thestate of Massachusetts
over the years into play.
Should a local repair shop
be touching advancedsoftware in the vehicle?
Once someone does touchthat advanced software,
who should be liable for that?
I mean, should a manufacturer be liable
when you change the chip set in your car
(29:56):
to something that itoriginally wasn't designed for?
Where are the bounds of liability in?
So these are huge conversations,
but are critical ifwe're gonna get our hands
around both repair
and the long-term costof ownership of vehicles.
And I for one, believe weare going to be driving
the vast majority of milestraveled in Western worlds
mostly the old fashioned way
with a little assistanceinvolved for years, decades,
(30:20):
if not the best part of a century or more.
- Okay, right becausechanging public perception
takes some time to get everyoneon the same page, of course.
- Just as a brief,practical follow up to that,
one of the things that we've discovered
as part of the AVTConsortium is unexpectedly,
we've learned a lot aboutthe complexity of repair.
It's been a very positiveaspect of our research study
(30:44):
with our fleets of vehiclesthat we've instrumented
and have people take out on the road.
We've had no serious accidents
in any of the vehicleswe've had on the roads
for the past 15 years.
But recently we have had a couple minor,
quote, unquote, "Fender benders,"
low-speed interactions,
little bit of crumple on the rear bumper
(31:05):
or the front bumper,
and we've run into this firsthand.
We've had the experience oftaking the vehicle to the dealer
where we got the vehicle from
and literally taken two to three months
to get the vehicle back on the road
because the dealer in fact is not equipped
(31:27):
to repair the sensors thatare in the bumper anymore.
More than that, the dealer is not equipped
to calibrate the sensors
that have been replaced in the vehicle.
And if the main dealer thatyou purchased the vehicle from
is not equipped for that, where do you go?
So it's a very large and important topic
(31:49):
that is quickly becoming very complex
and more significant foreveryone on a day by day basis.
- Well, and a very practicalthing for industry to take up
in terms of equipping their dealers
with the skills to repair these things.
- And Mackenzie, that'san interesting piece
is that where do the boundaries
of what dealers are notowned by the manufacturers,
they're independent.
And this is a hot button issuewith Tesla Service Centers,
(32:11):
Rivian, the new Scout brandthat's wholly owned by VW Group.
So when we're thinkingabout software systems
that are assisted orautomating vehicle features,
these are features thatare going to be deployed
in a new car and are today,
but are gonna have to exist
over the lifecycle of this vehicle.
What do they look like 10, 20 years later
when the windshield has begun to chip
(32:31):
and the camera can't seea clear view anymore?
Nobody knows.
And these are some ofthe white space issues
that I think are highlyrelevant to the future of safety
that we don't even know howto effectively study now
because quite frankly,
we haven't seen cars onthe road for long enough.
And I think these arethe big picture issues
we need to be thinking about.
- Right, which speaks again tothe importance of having AVT
to have this long-term data
(32:51):
right over the courseof these developments.
And looking at the impact
that y'all's work has had on the industry,
wondering if you couldspeak to some examples
of how this data hasinformed industry practices.
- One of the things
that does emerge fromthe data very early on
is something as simpleas video collection.
(33:14):
It is totally eye-opening for engineers
to go and look at these videos
and see what people are actuallyreally doing in the car.
Some of the human behavior that you see,
the engineer would never have considered
needing to plan for.
For example, we all have theimage of somebody picking up
(33:35):
and unfortunately tryingto text while they drive.
So there's some awareness of that,
and you do things in terms of develop
the driver monitoring systemto hopefully counter that.
But we've also seen things
like people are driving down the road
and not only picking up the phone to text,
but taking large size iPads
and setting them over the steering wheel
(33:58):
and working with themwhile they're driving,
and this is now covering up the airbag.
And if the airbag was redeployed
and pushed that iPad into the individual,
that would be a really,really high-risk situation.
So that's sort of one category
is actually seeing what humans do.
We do get a lot offeedback from our members,
(34:20):
that particular analysisor this set of video
or this sort of data hasbeen really, really useful,
but they don't always explain to us
exactly how that has been
because we're really getting into the area
of proprietary development.
So there are certain things
that members will talkabout together in the group.
And actually that's worthemphasizing for a moment.
(34:41):
One of the things that'sbeen amazing for us
is the AVT Consortium getstogether multiple times
during the year, bringing people together,
and we get into active discussions
of questions that areimportant to people today.
And of course, nobodyis compelled to speak up
from any given companyabout what their concern is,
(35:04):
issues that they're trying to address.
But we have found thatactually a number of companies
are very willing to speak up
and speak in very candid ways
about the kinds of challengesthey're confronting.
And it's amazing whatperson from company A says,
"This is a challenge we'vebeen struggling with,
and we have done X and Y to address it."
(35:27):
And then you see across the room,
person from company B says,
"Well, that's really interesting.
We've been working on that too,
and we've tried X and Y
and this is our thinking,
but it would be greatto dig in this deeper."
And the amount of cross-industryexchange in these meetings
has been a wonderful partof this whole process
(35:48):
of bringing this Consortium together.
- And I'll echo Bruce's words,
if you ask me what'sthe most impactful thing
I think has come from AVT,
it's the exchange of informationbetween the partners.
MIT in particular hasbeen known for decades
as a potential neutral space for industry
to come talk togetherabout research questions.
I believe the only waywe're going to compete
(36:08):
in the automotive industry forward
and compete with the growthof the Chinese auto industry
is by partnering andaccelerating innovation.
Innovation in the US is perhapsour most critical commodity,
and that's something I thinkMIT has done exceptionally well
over the decades from theWorld Wide Web Consortia
to many other efforts withinCTL and across campus.
(36:29):
And AVT is just another one
in a long list of case studies
where we are able to stimulate innovation
across an industry.
- And as you both said, to give this forum
that is not possible in the public space,
but only can happen in anonymity.
Let's dive deeper into the research,
looking at the methodsand the scientific process
to get this opportunitythat we have with you all
(36:50):
to look and see what'shappening behind the door.
Curious if you could speak tosome of the unique challenges
in measuring driver trustand reliance on automation
and how AVT ensures itsresearch remains rigorous,
while also being applicableto industry needs.
- So, trust is dynamic, right?
It changes over time
and depends heavily on the context.
(37:11):
So it's not enough to just ask drivers,
you know, once how muchthey trust the system.
That is exactly what isunique about our approach
is that we look at both what drivers say,
but also what they actually do.
- Yes, as in all areas of life.
Yes.(Pnina laughing)
- So yeah, we use behavioralproxies for trust and reliance,
(37:35):
things like how often drivers activate
or deactivate a feature,
how quickly they're reengaged
when they are prompt to do so,
or how frequently theyglance at a system display.
So we really look at how they interact
with the system in thereal world at scale.
(37:55):
We track these behaviors over long periods
across different road type situations,
system updates.
And that really helps us
to understand not justone moment of trust,
but how trust builds orbreaks down over time.
Of course, we strike a balance
(38:16):
between real-world complexitiesand research structure.
So as we mentioned multipletimes in this discussion,
we are collecting naturalistic data,
but we ground our analysisin clear hypothesis,
validated measures and replicable methods.
This we can offer insightsthat are scientifically sound
(38:38):
and directly useful forour industry partners
in designing these systems.
- In looking at y'all's research impact,
I know from our conversationthere's more that you can't say
than what you can in terms of specifics.
But Bryan, if you couldshare what's an example
of a key insight from your research
that changed the way companiesthink about automation?
- Yeah, Mackenzie, I think it starts
(39:00):
with a little bit of scientificdata goes a long way.
Most of the companieswe work with in industry
are often making good educated guesses
based upon very impartial information
because the developmentcycles are moving so quickly.
So having data at their fingertips
has helped a number of ourpartners accelerate innovation.
As our colleagues at Toyotahave discussed publicly,
(39:21):
there's a number of datasets
that have been created with them
that have been usedinternally to R&D processes.
But I think the most important impact
comes from some research mycolleague Pnina led here,
that automation is not set and forget.
The expectation with a lotof the assistive features
earlier on was you'regonna turn this feature on
(39:41):
at the beginning of the trip
or the beginning ofyour time in the highway
and you're gonna leave iton and it just space out.
And quite frankly, that's not true.
And some of the work that Pnina did
with one of her postdocs
really showed that we have adynamic exchange going on here.
At times I wanna drive,
at times I'd love to beassisted by the automation.
Well, I'm going to eat my cheeseburger
on the way home today,
and then turning on the automation
(40:01):
may be a great thingat that point in time.
So many of the systems like Autopilot
assume that you're gonnahave a hand on the wheel.
They're meant to assist the driver,
but the driver's supposed tokeep their hand on the wheel.
Other systems like GM Super Cruise,
Ford BlueCruise are really designed
to allow you to takeyour hands off the wheel.
Not everybody agrees,
but to me, designing forhands-free operation is critical.
(40:23):
Why?
Because the data clearly shows
even if you develop asystem to be hands-on,
people are taking their hands off anyway.
So if consumers are gonnatake their hands off,
we gotta design these systems
for what they're being used as.
And this really followsthe safe systems approach
that US DOT has been pushingover the last several years
is that we need to consider howfolks are using technologies
(40:45):
and design technologiesaround how they're being used
as opposed to a preconceived notion
of how we'd like them to use them.
And then again, this mirrors very much
where AVT has been over a decade or more.
- Right, responding to human behavior
as opposed to trying to dictate it.
So looking forward for thefuture of AVT and what's next,
where do you all see the future
(41:05):
of vehicle technology headingin the next, say, 5-10 years?
- Well, I'll start.
First of all, I believe thatwe will continue to see growth
and partial automation
and intelligent driver support systems.
These technologies will get smarter,
more adaptive, and more personalized,
and better at recognizing
(41:27):
when and how to engagewith the human driver.
At the same time,
I do believe in Level 4 automation,
and I think that we willsee meaningful progress
in that direction as well.
But I also think that the road there
is not just about refining the tech,
(41:48):
it's about buildingsystems that people trust,
understand and are ready to coexist with.
So my take on it
is that in the next 5-10 years,
we will see a future whereLevel 2 begins to scale
in selected applications
(42:08):
while the rest of themarket benefits from better,
smarter, safer and moreintuitive support system
that bridge this gap.
- So I see things in some ways
very synergistically with Pnina,
and in other ways a little divergently.
I do agree that assistive driving features
are a huge growth opportunity,
whether it's Level 2 features
(42:29):
or as the industry nomenclature goes,
Level 2+++++.
And the reason we keep adding pluses
is that we want to keep the driver engaged
and the driver responsible
for the oversight of these technologies.
I think ITS and connectivity services
that are coming to the vehicle,
either through the 5G network
or other communicating with each other
in the next 5-10 years
(42:50):
is gonna begin to take off a little bit.
Where I think that theautomation sphere is heading
is actually probablyless is more for a while.
I don't see the currentgrowth in Robotaxis
or highly automated trucking continuing.
I think these are investment models
that will begin to wind downover the next 5-10 years.
While they're growing it,
we believe huge scale now
(43:11):
the economics don't make sense
from a business perspective to me.
I think the big change inautomated driving to me
is probably a little further out
than the 5-10 year mark.
It's probably more in the 2-3 decade mark.
It's really highly pilot automation
where I turn a highlyautomated driving system on,
call it a Level 4 feature,
and a vehicle takesover all responsibility
(43:33):
for a period of time.
And I can go for 2-3 hours or more where,
or maybe not be able to fall asleep,
I might have to stayawake and somewhat alert.
But we begin to handle
the complexities of these transitions,
and the transitions being
that the vehicle seeks safe harbor.
If I don't intervene,
say maybe that's pullingover the side of the road
at a designated spot,pulling off the highway.
(43:53):
But this is a feature that I think
most of the auto industryis beginning to center on.
Is it the long-term focus isan auto automation feature
that can really relieve us.
So lots of folks say, "Well, that's hard."
Well, yeah, it is,
but right now Robotaxis are supported,
as I mentioned earlier, by teleoperation.
You know, someone in the back room.
The backroom operator hereis not a fallback ready user
(44:13):
that's there at any moment.
But the backroom operatorbecomes the person
in the operator's seat,
not expected to take over instantly,
but in the for of minutes or hours
they're there on the spot.
This is the feature that I think consumers
are willing to pay a lot for,
in essence giving me realfunctional time back,
but leaving the bulk ofthe driving off the highway
(44:33):
to the old-fashioned way.
And why the highway?
Because the highway isthe simplest domain.
So for the near future,
things aren't gonna change a whole lot,
a little more assistanceand a little more safety.
- I think one of the key terms
this has been used hereis that of driver support.
And I think that's really, really crucial
in terms of both improvingthe functionality
(44:55):
and improving the acceptanceof the semi-automated systems
that are in our vehicles nowand are are being refined.
One of the keys is that, as we've said,
for Level 2 driving and the like,
driver monitoring systemsbecome increasingly important.
The key is people are concerned
with the whole idea of, "Ohmy gosh, I'm being monitored."
(45:18):
And there are a couple of important topics
that have to be tackled there.
One of them of course is on privacy.
If this vehicle isgoing and monitoring me,
is my privacy being invaded?
Now, one of the things thatmost consumers don't realize
is that most of these systems,
and I will emphasize most,
are not actually saving any video data.
(45:42):
The video camera is sendingin a stream of information
that is processed to giveit certain information,
where a person is looking,are they looking, et cetera.
And that coded informationis being utilized,
but the video imageitself is not being saved.
So vehicle A doesn't have a record of,
(46:02):
"Oh my gosh, the argumentI had with my wife."
And so it's one importantfor people to know that.
At the same time,
there is a real role for the government
or other organizations toreally address this issue
that in fact that should be the standard.
Because in fact, there are other companies
(46:24):
to our understanding
where in some cases thevideo is being saved,
and that could be argued as inappropriate.
We really do need to have
a reasonable expectation of privacy.
Another kind of support comes to,
"Well, what does thedriving monitoring system
actually do with information?"
And not in the privacy sense,
(46:45):
but in terms of communicatingwith the driver.
There's been a historical emphasis
on introducing warningsystems into the vehicle.
So a lot of the monitoringsystems at the moment
are really emphasizing onbeeping and booping at me
when something is inappropriate or wrong
(47:06):
or I'm not paying attention or whatever.
The problem is, there are somany systems in the car now
when things go beep and boop,
it's like, "Where is that coming from?
You know, what thing isit trying to communicate?"
And we need to get out of the mode
of just always warning
to beginning to providemore of an information flow
(47:29):
so we are giving informationback to the driver
to help support themin the driving process.
So in fact, if I have lookedoff the road for too long
because I've gotten absorbed in something,
having a tone that hasa reasonable expectation
to catch my attention is one thing.
(47:50):
And if it's designed in a waythat I see that as a support
that, "Oh, that helped me
get my attention back to the road,"
that is gonna be both more functional
and more acceptable asopposed to, "Oh my gosh,
the car is buzzing at me again."
So I think that's areally important aspect
of moving from alarm to support.
(48:11):
- Yeah, a great point, right?
Because people begin to alsotune out those warnings.
Bruce, you spoke to how do we solve
for a lack of driver engagement
and situational awareness abit with a different approach
to the warning systems.
Pnina, what might you sayin terms of some solutions
to solve for responding toa lack of driver engagement
and situational awareness,
particularly with assistedand automated vehicles?
(48:34):
- So yeah, so one ofthe biggest challenges
in designing systems that keepdrivers appropriately engaged
with overwhelming or distractingthem is exactly that,
keeping them engaged.
Drivers are actively learninghow these technology behave
and in some cases learnhow to work around them
(48:56):
so that creates a movingtarget for designers,
basically to find a way to create systems
that are robust enough.
I do see this as an opportunity
with all the, especially AI applications
that are being introduced,
specifically large language models
(49:17):
that starts to enter intothe vehicle environment.
These systems open the doorfor more natural interactions
that could help keep drivers informed
and engage in meaningful ways.
So the sweet spot is a shared control,
where the vehicle supports the driver,
provides clear feedback
(49:38):
and adapts to the condition
or as the condition requires
without taking the human out of the loop.
- Yeah, absolutely.
So you all have reachedyour 10-year milestone,
what's next for the Consortium
in terms of researchfocus or new initiatives?
- Well, as we move forwardto our second decade,
(50:00):
we are expanding our focus
to few key new areas.
One is understanding how people adapt
to increasingly intelligent systems,
whether that's automation,electrification,
AI-based assistance in the vehicles.
Other directions arefocusing on personalization.
(50:23):
- I think that also oneof the intriguing things
about the size of the dataset
that we've been developingover the past 10 years,
one is that we are always going
and adding new vehicles to the fleet
to go and specifically look
at how new implementations of systems
have impact on driver behavior
(50:43):
and overall apparent safety.
So continuing to add vehicles
is an ongoing part ofthe fundamental process.
At the same time,
as the overall database grows,
we have the opportunity,
which we've been taking advantage of,
the increasing computerpower modeling approaches,
(51:07):
large language modeltechniques, et cetera,
we always have a continuing opportunity
as new ways of studying data
and analyzing data are developed,
is going back and usingthese new analytic techniques
on the existing dataset.
So not only can we continuously go
(51:28):
and begin to address new questions,
we can also use new tools
and apply them to the large set of data
that is always growing.
- Tomorrow is old newsa year or two from now.
And as we look forward,
as Pnina mentioned,into our second decade,
it's really continuing to evolve
in looking at theintersection of behavior,
(51:48):
technology and policy in new ways.
And we're very focused onwhat are the new white spaces
where we can enhance andfind safety gaps that exist
and help fill those ourselves
and with our partners involved
very much in the CTL model of partnership
with the organizations thatsupport and sponsor our work.
(upbeat music)
- That wraps up this episodeof "Supply Chain Frontiers."
(52:09):
A big thank you to Bryan Reimer,
Pnina Gershon, Bruce Mehler
for sharing their expertise and insights
into the evolving landscapeof vehicle technology
and its impact on supplychains and mobility.
To learn more about the AVT Consortium
and the research discussed today,
visit avt.mit.edu.
"Supply Chain Frontiers" isrecorded on the MIT Campus
(52:30):
in Cambridge, Massachusetts.
Our sound editors are Dave Lashinsky
and Danielle Simpson atDavid Benjamin Sound.
And our audio engineertoday is Kurt Schneider
of MIT Audio-Visual Services.
Our producer is myself, Mackenzie Berry.
Be sure to check out previous episodes
of "Supply Chain Frontiers"
at ctl.mit.edu/podcasts
(52:52):
or search for us on yourpreferred podcast platform.
If you enjoyed thisepisode, please subscribe.
I'm Mackenzie Berry,
thanks for listening,
and we'll catch you next timeon "Supply Chain Frontier."
(upbeat music continues)
(upbeat music)
- Welcome to another episodeof "Supply Chain Frontiers,"
the MIT CTL podcast
where we explore the trends,technologies and innovations
shaping the future ofsupply chain management.
I'm your host for thisepisode, Mackenzie Berry.
Today, we're diving into the world
of Advanced Vehicle Technology
and its implications forsupply chains and mobility.
(00:23):
Joining me are leading experts
from the MIT Center forTransportation & Logistics,
Dr. Bryan Reimer,
Founder and Co-director
of the Advanced VehicleTechnology Consortium,
Dr. Pnina Gershon,
Co-director of the AVT Consortium,
and Bruce Mehler,
Co-director of the AVT Consortium,
which is commemorating its10th anniversary this year.
(00:44):
Here's what I know.
AVT was founded with theaim of developing new data
that contributes toautomotive manufacturers',
suppliers' and insurers'real-world understanding
of how drivers use and respond
to increasingly sophisticatedvehicle technologies
such as assistive and automated driving
while accelerating theapplied insight needed
(01:04):
to advance design and development.
The Consortium has developeda deep understanding
of driver behavior andconsumer preferences
with technologies like Tesla's Autopilot,
GM's Super Cruise and Ford's BlueCruise,
as well as many other driver assistance
and support features.
Insights from AVT aimto help organizations
better design and market products
(01:24):
more closely aligning withreal-world consumer use
while advancing safe, convenientand sustainable mobility.
In this episode, Bryan, Pnina and Bruce
will explore key insightsfrom their recent research,
the role of data
in shaping safer andsmarter mobility solutions,
and how the Consortium isaddressing critical questions
around driver behavior,automation readiness,
(01:47):
and industry collaboration
as they celebrate theiranniversary milestone.
So welcome to the episode.
I'm so excited to have you all.
I introduced your titles,
but I would love if we could go around
and you all introduce yourself
with the work that you'lldo in the AVT Consortium.
- Once again, my name is Bruce Mehler,
I'm a Research Scientist
at the Center forTransportation & Logistics.
(02:08):
My own background is inpsychology and physiology,
and I've worked actually inthe medical device industry
for about 22 years before coming to MIT.
So I do have some appreciation
of the practical side of industry
in terms of actually having todevelop and produce product.
And I think that's areally important aspect
(02:29):
of working in a group like this at MIT,
where we're interested in bothhigh-quality academic work
that provides theoretical insights
while at the same time investing energy
and actually translating those insights
into operational informationthat industry can actually use.
(02:50):
And so I came to MIT aboutclose to 20 years ago now
and have been working inthis area since that time.
- Mackenzie, thanks for having us.
Bryan Reamer,
I've been a Research Scientist
within the Center forTransportation & Logistics
for about 22 years now.
I'm known as a pragmatic possibilist,
studying the intersection ofbehavior, technology and policy
(03:12):
at really the intersection of topics
around automation,assistive driving and AI.
Really focused on understanding
how we can encourage businessinnovation from research,
moving concept from the lab
to product innovations that touch us.
How do we create the ecosystems
that allow us to engagein the technologies
(03:32):
that can excite and delight the customer
and impact how we live and move
and leverage advanced technologies
as part of our daily lives?
So a lot of my background
comes from the study of driver behavior
and the evolution oftechnology in the auto sector
over the last two and a half decades.
- Hi, so my name is Pnina Gershon,
and I'm a ResearchScientist at the MIT AgeLab,
(03:53):
where I study how humansand technology interact
and how these interactions
impact mobility and driving safety.
I Co-direct the AdvancedVehicle Technology Consortium,
leading the research onnaturalistic driving.
So I bring many years ofexperience in data science,
AI, driving simulation,computational modeling,
(04:15):
and human physiology.
Before joining the AgeLab at MIT,
I was working on drivingsafety for high-risk population
at the National Institutes of Health,
and before that I was focused
on tactile and haptic interactions.
- Wonderful.
Bryan, I introduced the Consortium a bit,
(04:35):
but better to hear in your own words.
Can you give us a briefoverview of the Consortium,
how it started and whatits primary goals are?
- Sure, Mackenzie.
What we discovered withour industry partners
was that there was a lack of information
that existed in federaland research databases
around how consumers weretouching data in real cars
(04:56):
once they took ownership of the dealer.
So cars designed, developed
with incredible amounts of research,
investment, product development,
product planning, packaging,
and ultimately moving a car to the dealer.
But once the keys arehanded to the consumer,
there's very little understood,
and that white space waswhat AVT was built upon.
(05:18):
It was built as an academicindustry collaboration
informed by industry for industry.
How do we derive the insight
in what people areleveraging in their own lives
to help enhance thedevelopment of product,
the impact of the productsthat are being developed,
and the policies thatwill long-term govern
this industry forward?
(05:39):
It was captivated asAdvanced Vehicle Technology
because at that time we knew
automated, assisted were narrowly focused.
We wanna be thinking aboutthe wide swath of technology
that over the course of several decades
may be shaping how we live and move,
so very much the mobilitystory much more broadly.
- And if you could take usbehind the scenes a little bit,
(06:01):
what methodologies and research techniques
does the Consortium useto study driver behavior?
- So at the AVT Consortium,
we focus heavily onnaturalistic driving studies.
That means we are collectingdata from real people
driving real roads intheir own environments
and doing their regular routine.
(06:22):
So we take two main approaches for this.
The first we look atlong-term study of volunteers,
people that allow us toinstrument their own vehicles
with data acquisition systems.
Many of these participants havebeen with us for years now,
and that gives us a rare opportunity
(06:44):
to look at how behavior evolves over time,
so months and years.
We can study how peopleadapt to new features,
but also how they respondto software updates
and how trust in automation
evolves over these long periods of time.
Second, we run field operational tests,
(07:04):
which basically we haveour own vehicle fleet,
the MIT AgeLab vehicles,
in this case we loan our vehicles
like we have Volvo S90s,
Tesla Model 3s, Cadillac CT6, and so on.
So all of them are equipped
with Advanced Vehicle Technologies,
(07:25):
and participants use these cars
just like as if they woulduse their own vehicles
for their daily routine.
Now that's a great wayto learn how novices
or naive users get used to automation
or interact with automation.
So we look closely at early learning
(07:47):
and first impressionsand how people explore
or avoid using advanced technologies.
- Yeah, interesting.
Not only how they use it,
but what they avoid using as well,
which can tell you a lot.
Now, I imagine,
and I'm sure our listeners as well,
that you're dealingwith a lot of data here.
So curious, what are someof the biggest challenges
in working with large-scalenaturalistic driving data
(08:08):
and how do you ensure dataquality and reliability?
- Well, I think that, asyou said in the question,
the biggest challenge aroundworking with naturalistic data
is that it can be very, very large.
Particularly in our case,
depending on the vehiclethat we're working with,
we're using anywhere fromthree to four cameras
in the vehicle all the time.
(08:30):
And so if you can imagine
that literally you havehigh resolution cameras,
three or four in eachvehicle collecting data
every time the person goes and drives
for a month or two months or three months
or longer in time,
that data really builds up quickly.
And actually one of thethings that allows us
(08:52):
to pragmatically do thework that we're doing
is because of our investment through MIT
and through the state
in the Massachusetts HighPerformance Computing Center,
we actually have accessto a whole farm of servers
and large data storage.
In fact, if we didn't have access to that,
(09:14):
we could not work with the data
that's as large as we're using.
In fact, when we firststarted doing this work
and before we moved
to the High Performance Computing Center,
we were basically the second largest user
of storage space here on the MIT Campus.
I think astrophysics was the largest,
and all the rest of us were small by size,
(09:36):
but we were number two for a long time.
So once again, the biggest challenge
really is that size of the data,
processing the data, storing the data,
curating the data.
And I think in terms of data quality
and reliability insurance,
my colleagues Bryan or Pnina
may have something to add on that.
- Yeah, I mean that's a great question.
(09:57):
Honestly, this is somethingthat we think about every day
when you're collectingthis huge amount of data
and mixed data,
including video, audio,vehicle signals, GPS,
everything that Bruce just mentioned.
You're not just managingthe technical complexity,
but you also have the responsibility
(10:18):
to protect the people that volunteered
to share their driving with you.
So a lot of our effortis really technical,
but also basically making sure
that we have qualityassurance processes in place
that combine statistical tools
and human efforts toensure that the data we use
(10:38):
for research is intact and safe.
- I think one thing to add isthat the size of the datasets
that we collect are in thehundreds of terabytes range,
which by industry standards is modest,
by academic standards is extremely large.
It requires softwareengineering, data security,
computational investments
that are not traditionallyfound on college campuses.
(10:59):
And as Bruce mentioned earlier,
it's really MIT's forwardthinking investments
with the state of Massachusetts,
a few of the old colleges, universities
that have really enabledAVT to be what it is.
- And speaking of that,
you all just celebratedyour 10th anniversary,
which is a major milestone,celebrating it this year,
and held an event here on campus
(11:20):
which brought together major stakeholders
from across the automotive industry.
Curious, and if you could share
your biggest takeaway from the event.
- I think it was a sentinel event.
I think the goal that we had
was to really move the conversation
out of the analytical elements
that AVT has been focusing on or a decade
and move it back up a levelor two to the strategic level.
(11:40):
And the panels and keynotes
are all available off of our website,
so in long form.
But it really sharedwith me and highlighted
that the white space
in automotive safety research is shifting.
There's been a lot of focuson the use of technology
to try to encourage andimprove driver behavior,
but there is a lot of new elements
(12:02):
that technology is uncovering here.
And the repair vehicles gettingto be too expensive per se,
we're totaling way too many vehicles.
The context of sensor calibration,
when you do have a car
that needs this windshield replaced,
the amount of hours of labor
that needs to go intocalibrating those sensors
so they actually work correctly again
and the unknowns of a usedvehicle that you may purchase
(12:24):
and whether the sensors wereactually calibrated correctly
or the consumer found a runaround
to a cheaper repair facility.
Conversation with John Bozzella,CEO of Auto Innovators,
and Mark Rosekind, formerAdministrator of NITSA,
really focusing on the complexities
of vehicle safety policy looking forward.
(12:45):
But I think the most importanttakeaway and discussion point
is that the United Statesand the Western world
no longer leads in the area
of Advanced Vehicle Technologies,
China does.
The explosion of the Chinese auto industry
in the last five or more years
and the movement and acceleration
of assisted and automated driving features
(13:06):
into the fleet is astonishing.
The Chinese government back in late April,
beginning to crack down
and set some standardsaround assisted driving
that really lead the world,
thinking about the innovation in China
being curtailed by regulation
to a degree that in the United States
and most of the European nationshasn't been touched on yet.
(13:27):
So when we look forward,
we need to be thinking fromour perspective here at MIT
and in the United States ofhow do we accelerate forward?
And I believe very stronglythat collaborations
and partnerships are wherewe're gonna have to get there.
We're gonna have to bringindustry and academia,
industry and industry back together
to recapture our leadershipin this area quickly.
(13:49):
- Well, and as I understand it, Bruce,
you attended the Shanghai Auto Show
as well as the Detroit one.
What did you notice in termsof difference between the two
that Bryan had just spoke to?
- It was totally eye-opening.
As you said, I went to theDetroit Auto Show in January,
and it was a nice experience,
lots of interesting new cars on display,
lots of interesting booths and so on.
(14:11):
And then about two weeks
before our event in May,
I was in Shanghai
and went to the Auto Show there
and was bowled over.
The Auto Show in terms of space
was at least three to four times larger
than the Detroit Auto Show.
(14:33):
The number of vehicleswere three to four times
those that were presentin the Detroit Auto Show.
And it was amazing
looking at the evolutionthat's taken place,
not only in the vehicles themselves,
but the relationship betweenthe Chinese automakers
and the European automakers.
So in fact, there were anumber of companies there
(14:56):
presenting who did have partnerships
with the European automakers.
And you could see whathas changed over time
from the Europeanautomakers being the leaders
and having some slightlyless expensive versions
produced in China.
It's really turned around
where the Chinese automakers
(15:18):
who have still partnerships
were actually the predominantpeople at the show,
and their vehicles were larger,
fancier, equipped with more features
than their European partners.
So clearly a very rapidly changing space,
very much as Bryan has indicated.
One of the things that did come out
(15:40):
from both being at the Shanghai Auto Show,
but I also spent a couple weeks
at Tongji University in Shanghai,
which is really the leading university
for automotive studies in China.
And one of the interesting things
that came out from that
is talking to colleagues there
about the work that they were doing.
(16:00):
One of the areas that did stand out
where we are still leading in a sense
is that naturalistic data collection
in real vehicles by customers on the road
is relatively unknown at this point.
And so in fact, the AVTefforts in that particular area
of studying humaninteraction on the real road
(16:22):
with real drivers is stillan area where we are leading,
and it's important tocontinue that emphasis.
- Yeah, absolutely.
And turning to public perspective,
I imagine you all think alot about this in your work
and maybe have to dispel some things
among people that you know
right in conversation withthe expertise that you have,
but what are some misconceptionsabout vehicle automation
(16:42):
and driver behavior thatyou encounter most often?
- So one of the biggest misconception
is that automation makes driving passive.
In reality, most systems still require
an active driver and active supervision,
and drivers often dointeract with the automation.
Another is a belief
(17:03):
that more automationnecessarily mean more safety.
So driver behavior, trust,
and how systems are actually used
play a huge role in the outcome.
And automation can support the driver,
but it doesn't replace them,
so at least not yet.
- To me that we believe this aspect
(17:26):
of automation in vehicles is new.
It's not.
Everything from automatic transmissions
to power steering are thefoundations that this is built on.
So we've been actuallyinfusing automation into cars
in a variety of differentways for decades,
more and more, yeah.
But the offshoot ofthat is the expectation
that if you're gonna add more automation,
(17:48):
folks are going payattention the same way.
They're not going to,why are we automating?
We're automating to free resources.
Now the question is as humans,
how are we gonna behave?
How are we gonna use thoseresources to do other things?
And some of the data thatwe're beginning to see
and understand is that humansare capable of interacting
with the assistant systems in ways
(18:09):
that we wouldn't have expected early on.
So quite frankly, it'sless about the automation,
it's more about the interfacesand the support systems
and how to connect us to that automation.
Technology in and of itself
is not going to solve adriver behavior problem.
We keep adding more andmore technology to cars
with the hope and prayer
that it reduces accidents, mitigates harm.
(18:30):
The reality is we have to fit,
we have to begin to synctechnology focused very targetedly
at the behaviors we wanna optimize.
We, as humans, are notall above average drivers,
although I'm sure many of usaround the table here today
and listening are thinking
"Everybody's gotta be above average,"
but 50% is average.
- Yeah, absolutely.
No, I think we can all be on one page
that we don't like the Boston traffic
(18:51):
and the many drivers that we encounter,
I think everyone can agree with that.
- I'll take the Boston drivers
over the New York drivers every day.
- See that's a hot debate topic,
which city has the worst drivers?
Which people often debatein the public sphere.
Well, and public perceptionis a critical factor,
perhaps the most critical factor
in assisted and automatedvehicle adoption.
(19:12):
What does y'all's research suggest
about the biggest barriersto public acceptance?
- One of the biggest barriers
that's that's fairly well discussed openly
is consumers understandingof what assisted
and automated drivingis in the United States.
A recent piece out inthe last couple weeks
suggests that that's one of the bigger
differentiating piecesbetween consumers in China
(19:32):
and consumers in the States
is that consumers in China
really know what all this technology is,
and are getting a much more
mainstream understanding of that,
so they're demanding it.
And the US consumers don'treally have clear understanding
of what's assistive, what's automated,
and the fact that fullself-driving doesn't really exist.
So when we think about that barrier,
(19:53):
we really think about trust,
trust in the technologiesthat's appropriately calibrated
to the system's support for us,
and ensuring that we're notoverly trusting technology
when we, as a human,would be a better driver,
but we're leveraging that technology
where the automation cansupport us most effectively.
- I can add to that also predictability.
(20:13):
So people want to know whatthe system is gonna do,
and they want to feel confident
that it will behave the waythey expect it to behave.
So this is one another key thing,
and another thing is we need to make
automation more personal.
So seeing in a video or reading about it
is not the same as experiencing.
(20:36):
So a short ride,
10 minutes in a Waymo car, for example,
can do more to shift someone's perception
than hours of media coverage.
It's about helping people build
the firsthand understanding.
That's where real acceptance starts.
(20:57):
- And can you share for listeners
who may not know what Waymo is?
- So Waymo is a Level 4 vehicle
basically in certain areasin the United States,
these vehicles are deployed,
so basically you canschedule a ride with them
and fully autonomous,
there is no driver behind the wheel.
(21:17):
- Yeah, very scary for some people still,
which is why we're here.
But Bruce, what about for you
in terms of what research suggests
about the biggest barriersof public acceptance?
- One of the challenges that we've seen
with the introduction of new technologies
is how the consumer develops expectations
about how a system will work,
how do you actually use it?
(21:38):
In fact, there's another aspectthere of, "How do I use it?
How do I turn it on?"
So there's many technologies in the car
that may be very well engineered
in terms of their underlying function,
but quite often there are challenges
in terms of the user interface.
Obviously, the user interfacesare developed by engineers
(22:00):
who spent hours and hours
thinking about anddeveloping these interfaces,
and they understandhow the interface works
in and out, right and left
so that everything of course
is, "Intuitive," quote, unquote,
because they've beenworking with it for years.
And it's that classic challenge
(22:22):
just as you find in computer systems,
quite often when thenew feature comes out,
it's intuitive to the designer
because they've worked on designing it,
but it may or may not beintuitive to the end user.
If they are actually walked through it,
they go, "Oh, okay,
now I understand howthis is meant to work."
(22:44):
But unless we're given appropriate support
to help walk us throughthis new technology,
it's quite often the case
that a number of people will never use it.
And we also know very well
that once a person gets,for example, a new car,
if they don't try out a technology
(23:05):
in the first three or four weeks,
there's a very, very high probability
they will never turn it on
for the length of timethat they own their car.
And so just as it's a challenge
with computer technologies and the like,
in the vehicle,
the whole issue designing user interfaces
and finding ways to help talk the user
(23:27):
through the feature so they can try it out
and then intelligently decide
whether or not they want to use it or not.
And that human factors interface
of helping introduce and explain features
to get people to try them
is really a key challengefor the industry.
- I think one thing toadd to my colleagues here
(23:48):
is that when you thinkabout barriers here,
we really have highlyautomated technologies
on the road today in a number of cities.
Ms. Pnina described
most of these systems are supported
extensively by teleoperation,
someone in the back office
whose helping these robots on the road.
The barrier to public acceptance to me
is how safe is safe enough?
(24:09):
We have no yardstick on how safe
assisted and automated technologies
need to be to be accepted.
And one of the things that I call
on the regulatory side for
is helping us form that social norm
because a technology thatcan help improve safety
by three or four times what it is today
is something we probablyshould be endorsing.
And the question is, isthat safe enough tomorrow?
(24:31):
And my answer is no, we needto be doing better tomorrow.
But picking some reasonable targets
and iterating from that
and enhancing public acceptance over time.
Otherwise, every incident there is,
is a major news media circus,
and that's really the key to avoiding,
to enhancing publicacceptance of this technology.
- Yeah, well, this is a great segue
because speaking of regulation,
(24:52):
how are government policy and regulation
influencing Advanced Vehicle Technology
development and adoption?
You spoke to it a little bit,
but if you could expand.
- So unfortunately,government policies today
are not as effectively asnon-government policies
enhancing regulatory adoptionof these technologies.
So organizations likethe Insurance Institute
Highway Safety Consumer Reports,
Euro NCAP,
(25:13):
more non-government organizations
that are either encouraging the adoption
of these technologies a little faster.
We saw late in the last administration
reforms in the US NCAP program,
which is really NITSA,
National Highway TransportationSafety Administration,
their effort to proactivelypromote these technologies,
but it's a little later than optimal.
So it's really about using the carrot
(25:36):
and the education sideof promoting technologies
that we don't truly understandthe safety benefit of.
And that's where the whitespace I talked earlier
that we discovered nearlya decade and a half ago
was that many of these systems,
well, we believe they're good,
we don't know until the datais developed years later.
So one of the pieces several years ago
(25:56):
during the Obama administration
was the collaborative agreementbetween the automakers,
NITSA and IHS around the the deployment
of automated emergency braking systems,
which was really a Hail Mary pass
at trying to accelerate thesetechnologies on the road.
So at the end of the day,
we see the NGOs takingmore of a leadership
than the government here.
(upbeat music)
- This episode of "Supply Chain Frontiers"
(26:18):
is brought to you by theMITx MicroMasters program
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Visit micromasters.mit.edu/scm
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(upbeat music continues)
(26:41):
Bryan, you mentioned this earlier,
the cost of repairs
as technology becomesmore advanced in vehicles.
One of the panels that interestedme most at the AVT event
was the collision repair panel,
which was really meantto represent consumers,
and they spoke to the high cost of repair
as well as the insurance for vehicles
with assisted and automated technology.
What seems to be the consumerperception in this regard
(27:01):
and how should industry respond?
- Consumer perception isthat cars are repaired
much like they used to be,
and unfortunately they're not.
These are highly complex technical systems
integrating together a little sheet metal,
a little aluminum,
lots of different forms of plastic,
sensing technologies, compute, wiring.
These are among the mostcomplex technologies
that we as humans engagewith on a daily basis.
(27:25):
So when something happens,
it's not going and justbanging out the metal
and reforming the structureof the car that way,
it's about rebuildingthe sensor architecture
that was there to protect you
in the case of an adverse event.
So when we think about repair today,
it's not auto body repair,what it used to be.
It's bringing in a bunch of engineers
to figure out, "How do we address this?
(27:46):
Can we find the parts?
Where might those parts be coming from?"
And then quite frankly,
calibrating the systems again,
and hours and hoursand investment to that.
Things that take minutes
when the car is flowingoff the assembly line
take hours to recreate later on.
So when consumers think aboutrisk and insurance costs,
there's no mystery that thecost of ownership of a vehicle
(28:09):
starts with your purchase
if you can afford topurchase or lease a car,
but the cost of insuring that
and the lifecycle costs of that vehicle
are driven by the complexities
of sensing and compute technology.
So I think one of thethings that consumers
really don't recognize enough
is that the auto industry isadvancing in material sciences
at an accelerated pace.
(28:30):
How do you best way to save gas
is to make the vehicle lighter,
so huge efforts in lightweighting.
And the same is really therein a lot of other logistics
avenues as well, whetherthat's long haul trucking
or that's local delivery
or robots chasing us around on streets
to deliver packages in some cities today.
- Yeah, how do you thinkindustry should respond
given the higher costsnow on the consumers?
- I think it starts witha strategic conversation,
(28:52):
being willing to talk across the industry
on how do we find newstrategic pathways forward?
Quite frankly, most of theorganizations that I talk to
know there is a cost problem.
The question is how do you beginto address complex problems
that require so manystakeholders involved?
Each organization only owns
a little piece of this complexity.
(29:13):
So it's moving from a oneindustry talking to another
or one partner talking to another
to how do you get key players in a room
to have a real big picture conversation,
setting strategy forward
where we're really thinking about repaving
or restructuring how we lookat the cost of ownership,
the cost of repair of vehicles,
maybe even the inspection
and licensure of repair's facilities.
(29:35):
The technologies requiredare far different.
And this brings all topicssuch as right to repair,
which has been hot in thestate of Massachusetts
over the years into play.
Should a local repair shop
be touching advancedsoftware in the vehicle?
Once someone does touchthat advanced software,
who should be liable for that?
I mean, should a manufacturer be liable
when you change the chip set in your car
(29:56):
to something that itoriginally wasn't designed for?
Where are the bounds of liability in?
So these are huge conversations,
but are critical ifwe're gonna get our hands
around both repair
and the long-term costof ownership of vehicles.
And I for one, believe weare going to be driving
the vast majority of milestraveled in Western worlds
mostly the old fashioned way
with a little assistanceinvolved for years, decades,
(30:20):
if not the best part of a century or more.
- Okay, right becausechanging public perception
takes some time to get everyoneon the same page, of course.
- Just as a brief,practical follow up to that,
one of the things that we've discovered
as part of the AVTConsortium is unexpectedly,
we've learned a lot aboutthe complexity of repair.
It's been a very positiveaspect of our research study
(30:44):
with our fleets of vehiclesthat we've instrumented
and have people take out on the road.
We've had no serious accidents
in any of the vehicleswe've had on the roads
for the past 15 years.
But recently we have had a couple minor,
quote, unquote, "Fender benders,"
low-speed interactions,
little bit of crumple on the rear bumper
(31:05):
or the front bumper,
and we've run into this firsthand.
We've had the experience oftaking the vehicle to the dealer
where we got the vehicle from
and literally taken two to three months
to get the vehicle back on the road
because the dealer in fact is not equipped
(31:27):
to repair the sensors thatare in the bumper anymore.
More than that, the dealer is not equipped
to calibrate the sensors
that have been replaced in the vehicle.
And if the main dealer thatyou purchased the vehicle from
is not equipped for that, where do you go?
So it's a very large and important topic
(31:49):
that is quickly becoming very complex
and more significant foreveryone on a day by day basis.
- Well, and a very practicalthing for industry to take up
in terms of equipping their dealers
with the skills to repair these things.
- And Mackenzie, that'san interesting piece
is that where do the boundaries
of what dealers are notowned by the manufacturers,
they're independent.
And this is a hot button issuewith Tesla Service Centers,
(32:11):
Rivian, the new Scout brandthat's wholly owned by VW Group.
So when we're thinkingabout software systems
that are assisted orautomating vehicle features,
these are features thatare going to be deployed
in a new car and are today,
but are gonna have to exist
over the lifecycle of this vehicle.
What do they look like 10, 20 years later
when the windshield has begun to chip
(32:31):
and the camera can't seea clear view anymore?
Nobody knows.
And these are some ofthe white space issues
that I think are highlyrelevant to the future of safety
that we don't even know howto effectively study now
because quite frankly,
we haven't seen cars onthe road for long enough.
And I think these arethe big picture issues
we need to be thinking about.
- Right, which speaks again tothe importance of having AVT
to have this long-term data
(32:51):
right over the courseof these developments.
And looking at the impact
that y'all's work has had on the industry,
wondering if you couldspeak to some examples
of how this data hasinformed industry practices.
- One of the things
that does emerge fromthe data very early on
is something as simpleas video collection.
(33:14):
It is totally eye-opening for engineers
to go and look at these videos
and see what people are actuallyreally doing in the car.
Some of the human behavior that you see,
the engineer would never have considered
needing to plan for.
For example, we all have theimage of somebody picking up
(33:35):
and unfortunately tryingto text while they drive.
So there's some awareness of that,
and you do things in terms of develop
the driver monitoring systemto hopefully counter that.
But we've also seen things
like people are driving down the road
and not only picking up the phone to text,
but taking large size iPads
and setting them over the steering wheel
(33:58):
and working with themwhile they're driving,
and this is now covering up the airbag.
And if the airbag was redeployed
and pushed that iPad into the individual,
that would be a really,really high-risk situation.
So that's sort of one category
is actually seeing what humans do.
We do get a lot offeedback from our members,
(34:20):
that particular analysisor this set of video
or this sort of data hasbeen really, really useful,
but they don't always explain to us
exactly how that has been
because we're really getting into the area
of proprietary development.
So there are certain things
that members will talkabout together in the group.
And actually that's worthemphasizing for a moment.
(34:41):
One of the things that'sbeen amazing for us
is the AVT Consortium getstogether multiple times
during the year, bringing people together,
and we get into active discussions
of questions that areimportant to people today.
And of course, nobodyis compelled to speak up
from any given companyabout what their concern is,
(35:04):
issues that they're trying to address.
But we have found thatactually a number of companies
are very willing to speak up
and speak in very candid ways
about the kinds of challengesthey're confronting.
And it's amazing whatperson from company A says,
"This is a challenge we'vebeen struggling with,
and we have done X and Y to address it."
(35:27):
And then you see across the room,
person from company B says,
"Well, that's really interesting.
We've been working on that too,
and we've tried X and Y
and this is our thinking,
but it would be greatto dig in this deeper."
And the amount of cross-industryexchange in these meetings
has been a wonderful partof this whole process
(35:48):
of bringing this Consortium together.
- And I'll echo Bruce's words,
if you ask me what'sthe most impactful thing
I think has come from AVT,
it's the exchange of informationbetween the partners.
MIT in particular hasbeen known for decades
as a potential neutral space for industry
to come talk togetherabout research questions.
I believe the only waywe're going to compete
(36:08):
in the automotive industry forward
and compete with the growthof the Chinese auto industry
is by partnering andaccelerating innovation.
Innovation in the US is perhapsour most critical commodity,
and that's something I thinkMIT has done exceptionally well
over the decades from theWorld Wide Web Consortia
to many other efforts withinCTL and across campus.
(36:29):
And AVT is just another one
in a long list of case studies
where we are able to stimulate innovation
across an industry.
- And as you both said, to give this forum
that is not possible in the public space,
but only can happen in anonymity.
Let's dive deeper into the research,
looking at the methodsand the scientific process
to get this opportunitythat we have with you all
(36:50):
to look and see what'shappening behind the door.
Curious if you could speak tosome of the unique challenges
in measuring driver trustand reliance on automation
and how AVT ensures itsresearch remains rigorous,
while also being applicableto industry needs.
- So, trust is dynamic, right?
It changes over time
and depends heavily on the context.
(37:11):
So it's not enough to just ask drivers,
you know, once how muchthey trust the system.
That is exactly what isunique about our approach
is that we look at both what drivers say,
but also what they actually do.
- Yes, as in all areas of life.
Yes.(Pnina laughing)
- So yeah, we use behavioralproxies for trust and reliance,
(37:35):
things like how often drivers activate
or deactivate a feature,
how quickly they're reengaged
when they are prompt to do so,
or how frequently theyglance at a system display.
So we really look at how they interact
with the system in thereal world at scale.
(37:55):
We track these behaviors over long periods
across different road type situations,
system updates.
And that really helps us
to understand not justone moment of trust,
but how trust builds orbreaks down over time.
Of course, we strike a balance
(38:16):
between real-world complexitiesand research structure.
So as we mentioned multipletimes in this discussion,
we are collecting naturalistic data,
but we ground our analysisin clear hypothesis,
validated measures and replicable methods.
This we can offer insightsthat are scientifically sound
(38:38):
and directly useful forour industry partners
in designing these systems.
- In looking at y'all's research impact,
I know from our conversationthere's more that you can't say
than what you can in terms of specifics.
But Bryan, if you couldshare what's an example
of a key insight from your research
that changed the way companiesthink about automation?
- Yeah, Mackenzie, I think it starts
(39:00):
with a little bit of scientificdata goes a long way.
Most of the companieswe work with in industry
are often making good educated guesses
based upon very impartial information
because the developmentcycles are moving so quickly.
So having data at their fingertips
has helped a number of ourpartners accelerate innovation.
As our colleagues at Toyotahave discussed publicly,
(39:21):
there's a number of datasets
that have been created with them
that have been usedinternally to R&D processes.
But I think the most important impact
comes from some research mycolleague Pnina led here,
that automation is not set and forget.
The expectation with a lotof the assistive features
earlier on was you'regonna turn this feature on
(39:41):
at the beginning of the trip
or the beginning ofyour time in the highway
and you're gonna leave iton and it just space out.
And quite frankly, that's not true.
And some of the work that Pnina did
with one of her postdocs
really showed that we have adynamic exchange going on here.
At times I wanna drive,
at times I'd love to beassisted by the automation.
Well, I'm going to eat my cheeseburger
on the way home today,
and then turning on the automation
(40:01):
may be a great thingat that point in time.
So many of the systems like Autopilot
assume that you're gonnahave a hand on the wheel.
They're meant to assist the driver,
but the driver's supposed tokeep their hand on the wheel.
Other systems like GM Super Cruise,
Ford BlueCruise are really designed
to allow you to takeyour hands off the wheel.
Not everybody agrees,
but to me, designing forhands-free operation is critical.
(40:23):
Why?
Because the data clearly shows
even if you develop asystem to be hands-on,
people are taking their hands off anyway.
So if consumers are gonnatake their hands off,
we gotta design these systems
for what they're being used as.
And this really followsthe safe systems approach
that US DOT has been pushingover the last several years
is that we need to consider howfolks are using technologies
(40:45):
and design technologiesaround how they're being used
as opposed to a preconceived notion
of how we'd like them to use them.
And then again, this mirrors very much
where AVT has been over a decade or more.
- Right, responding to human behavior
as opposed to trying to dictate it.
So looking forward for thefuture of AVT and what's next,
where do you all see the future
(41:05):
of vehicle technology headingin the next, say, 5-10 years?
- Well, I'll start.
First of all, I believe thatwe will continue to see growth
and partial automation
and intelligent driver support systems.
These technologies will get smarter,
more adaptive, and more personalized,
and better at recognizing
(41:27):
when and how to engagewith the human driver.
At the same time,
I do believe in Level 4 automation,
and I think that we willsee meaningful progress
in that direction as well.
But I also think that the road there
is not just about refining the tech,
(41:48):
it's about buildingsystems that people trust,
understand and are ready to coexist with.
So my take on it
is that in the next 5-10 years,
we will see a future whereLevel 2 begins to scale
in selected applications
(42:08):
while the rest of themarket benefits from better,
smarter, safer and moreintuitive support system
that bridge this gap.
- So I see things in some ways
very synergistically with Pnina,
and in other ways a little divergently.
I do agree that assistive driving features
are a huge growth opportunity,
whether it's Level 2 features
(42:29):
or as the industry nomenclature goes,
Level 2+++++.
And the reason we keep adding pluses
is that we want to keep the driver engaged
and the driver responsible
for the oversight of these technologies.
I think ITS and connectivity services
that are coming to the vehicle,
either through the 5G network
or other communicating with each other
in the next 5-10 years
(42:50):
is gonna begin to take off a little bit.
Where I think that theautomation sphere is heading
is actually probablyless is more for a while.
I don't see the currentgrowth in Robotaxis
or highly automated trucking continuing.
I think these are investment models
that will begin to wind downover the next 5-10 years.
While they're growing it,
we believe huge scale now
(43:11):
the economics don't make sense
from a business perspective to me.
I think the big change inautomated driving to me
is probably a little further out
than the 5-10 year mark.
It's probably more in the 2-3 decade mark.
It's really highly pilot automation
where I turn a highlyautomated driving system on,
call it a Level 4 feature,
and a vehicle takesover all responsibility
(43:33):
for a period of time.
And I can go for 2-3 hours or more where,
or maybe not be able to fall asleep,
I might have to stayawake and somewhat alert.
But we begin to handle
the complexities of these transitions,
and the transitions being
that the vehicle seeks safe harbor.
If I don't intervene,
say maybe that's pullingover the side of the road
at a designated spot,pulling off the highway.
(43:53):
But this is a feature that I think
most of the auto industryis beginning to center on.
Is it the long-term focus isan auto automation feature
that can really relieve us.
So lots of folks say, "Well, that's hard."
Well, yeah, it is,
but right now Robotaxis are supported,
as I mentioned earlier, by teleoperation.
You know, someone in the back room.
The backroom operator hereis not a fallback ready user
(44:13):
that's there at any moment.
But the backroom operatorbecomes the person
in the operator's seat,
not expected to take over instantly,
but in the for of minutes or hours
they're there on the spot.
This is the feature that I think consumers
are willing to pay a lot for,
in essence giving me realfunctional time back,
but leaving the bulk ofthe driving off the highway
(44:33):
to the old-fashioned way.
And why the highway?
Because the highway isthe simplest domain.
So for the near future,
things aren't gonna change a whole lot,
a little more assistanceand a little more safety.
- I think one of the key terms
this has been used hereis that of driver support.
And I think that's really, really crucial
in terms of both improvingthe functionality
(44:55):
and improving the acceptanceof the semi-automated systems
that are in our vehicles nowand are are being refined.
One of the keys is that, as we've said,
for Level 2 driving and the like,
driver monitoring systemsbecome increasingly important.
The key is people are concerned
with the whole idea of, "Ohmy gosh, I'm being monitored."
(45:18):
And there are a couple of important topics
that have to be tackled there.
One of them of course is on privacy.
If this vehicle isgoing and monitoring me,
is my privacy being invaded?
Now, one of the things thatmost consumers don't realize
is that most of these systems,
and I will emphasize most,
are not actually saving any video data.
(45:42):
The video camera is sendingin a stream of information
that is processed to giveit certain information,
where a person is looking,are they looking, et cetera.
And that coded informationis being utilized,
but the video imageitself is not being saved.
So vehicle A doesn't have a record of,
(46:02):
"Oh my gosh, the argumentI had with my wife."
And so it's one importantfor people to know that.
At the same time,
there is a real role for the government
or other organizations toreally address this issue
that in fact that should be the standard.
Because in fact, there are other companies
(46:24):
to our understanding
where in some cases thevideo is being saved,
and that could be argued as inappropriate.
We really do need to have
a reasonable expectation of privacy.
Another kind of support comes to,
"Well, what does thedriving monitoring system
actually do with information?"
And not in the privacy sense,
(46:45):
but in terms of communicatingwith the driver.
There's been a historical emphasis
on introducing warningsystems into the vehicle.
So a lot of the monitoringsystems at the moment
are really emphasizing onbeeping and booping at me
when something is inappropriate or wrong
(47:06):
or I'm not paying attention or whatever.
The problem is, there are somany systems in the car now
when things go beep and boop,
it's like, "Where is that coming from?
You know, what thing isit trying to communicate?"
And we need to get out of the mode
of just always warning
to beginning to providemore of an information flow
(47:29):
so we are giving informationback to the driver
to help support themin the driving process.
So in fact, if I have lookedoff the road for too long
because I've gotten absorbed in something,
having a tone that hasa reasonable expectation
to catch my attention is one thing.
(47:50):
And if it's designed in a waythat I see that as a support
that, "Oh, that helped me
get my attention back to the road,"
that is gonna be both more functional
and more acceptable asopposed to, "Oh my gosh,
the car is buzzing at me again."
So I think that's areally important aspect
of moving from alarm to support.
(48:11):
- Yeah, a great point, right?
Because people begin to alsotune out those warnings.
Bruce, you spoke to how do we solve
for a lack of driver engagement
and situational awareness abit with a different approach
to the warning systems.
Pnina, what might you sayin terms of some solutions
to solve for responding toa lack of driver engagement
and situational awareness,
particularly with assistedand automated vehicles?
(48:34):
- So yeah, so one ofthe biggest challenges
in designing systems that keepdrivers appropriately engaged
with overwhelming or distractingthem is exactly that,
keeping them engaged.
Drivers are actively learninghow these technology behave
and in some cases learnhow to work around them
(48:56):
so that creates a movingtarget for designers,
basically to find a way to create systems
that are robust enough.
I do see this as an opportunity
with all the, especially AI applications
that are being introduced,
specifically large language models
(49:17):
that starts to enter intothe vehicle environment.
These systems open the doorfor more natural interactions
that could help keep drivers informed
and engage in meaningful ways.
So the sweet spot is a shared control,
where the vehicle supports the driver,
provides clear feedback
(49:38):
and adapts to the condition
or as the condition requires
without taking the human out of the loop.
- Yeah, absolutely.
So you all have reachedyour 10-year milestone,
what's next for the Consortium
in terms of researchfocus or new initiatives?
- Well, as we move forwardto our second decade,
(50:00):
we are expanding our focus
to few key new areas.
One is understanding how people adapt
to increasingly intelligent systems,
whether that's automation,electrification,
AI-based assistance in the vehicles.
Other directions arefocusing on personalization.
(50:23):
- I think that also oneof the intriguing things
about the size of the dataset
that we've been developingover the past 10 years,
one is that we are always going
and adding new vehicles to the fleet
to go and specifically look
at how new implementations of systems
have impact on driver behavior
(50:43):
and overall apparent safety.
So continuing to add vehicles
is an ongoing part ofthe fundamental process.
At the same time,
as the overall database grows,
we have the opportunity,
which we've been taking advantage of,
the increasing computerpower modeling approaches,
(51:07):
large language modeltechniques, et cetera,
we always have a continuing opportunity
as new ways of studying data
and analyzing data are developed,
is going back and usingthese new analytic techniques
on the existing dataset.
So not only can we continuously go
(51:28):
and begin to address new questions,
we can also use new tools
and apply them to the large set of data
that is always growing.
- Tomorrow is old newsa year or two from now.
And as we look forward,
as Pnina mentioned,into our second decade,
it's really continuing to evolve
in looking at theintersection of behavior,
(51:48):
technology and policy in new ways.
And we're very focused onwhat are the new white spaces
where we can enhance andfind safety gaps that exist
and help fill those ourselves
and with our partners involved
very much in the CTL model of partnership
with the organizations thatsupport and sponsor our work.
(upbeat music)
- That wraps up this episodeof "Supply Chain Frontiers."
(52:09):
A big thank you to Bryan Reimer,
Pnina Gershon, Bruce Mehler
for sharing their expertise and insights
into the evolving landscapeof vehicle technology
and its impact on supplychains and mobility.
To learn more about the AVT Consortium
and the research discussed today,
visit avt.mit.edu.
"Supply Chain Frontiers" isrecorded on the MIT Campus
(52:30):
in Cambridge, Massachusetts.
Our sound editors are Dave Lashinsky
and Danielle Simpson atDavid Benjamin Sound.
And our audio engineertoday is Kurt Schneider
of MIT Audio-Visual Services.
Our producer is myself, Mackenzie Berry.
Be sure to check out previous episodes
of "Supply Chain Frontiers"
at ctl.mit.edu/podcasts
(52:52):
or search for us on yourpreferred podcast platform.
If you enjoyed thisepisode, please subscribe.
I'm Mackenzie Berry,
thanks for listening,
and we'll catch you next timeon "Supply Chain Frontier."
(upbeat music continues)
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